Detection of fault in Railway track using Image processing and Fuzzy logic

The transportation route is being impacted by the elaborate development of railway transportation vehicles. Many errors happen Due to the use of train lines. Both manufacturing mistakes and improper rail usage are to blame for these failures. There are numerous techniques for spotting errors early and fixing them. The camera-based approach is one of these techniques. By using the cameras mounted to the railway vehicle, pictures taken of the rail components are inspected. The components of the rail show faults are found. A technique for identifying and categorizing flaws on rail track surfaces is suggested in this paper. The suggested approach utilized image processing to identify the rail surface. High resolution photos taken by specialized cameras mounted on railway inspection cars are used in the proposed system. A variety of track issues, including fractures, weld flaws, track misalignment, and ballast degradation, are detected and analyzed using these photos. Pre-processing, feature extraction, and segmentation algorithms are used in the image processing procedures to isolate the track region and highlight any potential flaws. Fuzzy logic is used to rank maintenance tasks according to priority after defects have been found and their severity has been determined. Fuzzy logic is well suited for capturing the subjective judgements involved in evaluating track conditions because it offers a flexible framework for processing ambiguous and imprecise data. To assign the proper severity levels to the extracted features of each defect type, fuzzy rules and membership functions are constructed.